ObjectiveTo investigate the association between immunoglobulin G （IgG）/immunoglobulin M （IgM） ratio and prognosis in patients with initially unresectable hepatocellular carcinoma （iuHCC） receiving TTP triple therapy with transcatheter arterial chemoembolization （TACE）， tyrosine kinase inhibitor （TKI）， and programmed cell death protein-1 （PD-1） inhibitors. MethodsA retrospective analysis was performed for the clinical data of 151 iuHCC patients who received TTP triple therapy in Department of Hepatobiliary Surgery， Guangxi Medical University Cancer Hospital， from November 2019 to December 2022， and according to IgG/IgM ratio， they were divided into high IgG/IgM group （IgG/IgM ratio >13.23） and low IgG/IgM group （IgG/IgM ratio ≤13.23）. The t-test was used for comparison of continuous data between groups， and the chi-square test was used for comparison of categorical data between groups. The Kaplan-Meier method and the log-rank test were used for survival analysis， and the Cox proportional hazards model was used to investigate the potential influencing factors for overall survival （OS）. ResultsThe 151 patients had a median OS of 26.7 months （95% confidence interval ［CI］： 19.8-not reached） and a median progression-free survival of 12.5 months （95%CI： 10.4 — 15.8）. The objective response rate was 83.4% and the disease control rate was 94.0%. There were no significant differences in baseline data between the high IgG/IgM group and the low IgG/IgM group （all P>0.05）. There was a significant difference in median OS between the high IgG/IgM group and the low IgG/IgM group （20.6 months vs not reached， P=0.016）. In both the high IgG/IgM group and the low IgG/IgM group， salvage hepatectomy was significantly associated with the improvement in OS （χ²=8.297 and 10.307， both P<0.05）. The multivariate analysis showed that high IgG/IgM ratio （hazard ratio ［HR］=1.799， 95%CI： 1.077 — 3.006， P=0.025）， baseline alpha-fetoprotein >400 ng/mL （HR=1.762， 95%CI： 1.017 — 3.050， P=0.043）， and BCLC stage （HR=2.265， 95%CI： 1.212 — 4.232， P=0.010） were independent influencing factors for OS. ConclusionHigh IgG/IgM ratio is associated with a poorer prognosis in iuHCC patients receiving TTP triple therapy， and salvage hepatectomy has a potential value in improving the prognosis of patients with a high IgG/IGM ratio.

Objective:To investigate the clinical value of low molecular weight heparin bridging therapy for patients undergoing inguinal hernia repair who with long-term oral antiplatelet agents.Methods:The propensity score matching and retrospective cohort study was conducted. The clinical data of 126 patients undergoing tension-free inguinal hernia repair who with long-term oral antiplatelet agents and admitted to Sichuan Academy of Medical Sciences & Sichuan Provincial People′s Hospital (Affiliated Hospital of University of Electronic Science and Technology of China) from January 2017 to January 2025 were collected. There were 120 males and 6 females, aged (74±9)years. Of the 126 patients, 77 patients who discontinued antiplatelet agents alone before inguinal hernia repair were set as the drug withdrawal group, and 49 patients who discontinued antiplatelet agents with low molecular weight heparin bridging therapy before inguinal hernia repair were set as the bridging group. Observation indicators: (1) propensity score matching and comparison of general data of patients between the two groups after matching; (2) intraoperative and postopera-tive conditions; (3) follow-up. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test or Fisher exact probability. Propensity score matching was performed using the 1∶1 nearest neighbor matching method. The caliper value was set as 0.1. Results:(1) Propensity score matching and comparison of general data of patients between the two groups after matching. Of the 126 patients, 90 patients were success-fully matched, with 45 cases in each of the drug withdrawal group and the bridging group. After propensity score matching, the elimination of hernia ring size, activated partial thromboplasmin time and surgical method factors confounding bias ensured comparability. (2) Intraoperative and postoperative conditions. After propensity score matching, patients using plasma drainage tubes during the operation in the drug withdrawal group and the bridging group were 8 and 1, respec-tively, showing a significant difference between the two groups ( P<0.05). The visual analogue scale scores of patients in the drug withdrawal group and the bridging group at 48 hours after surgery were 2(range, 1-2) and 2(range, 2-3), respectively, showing a significant difference between the two groups ( Z=-2.57, P<0.05). (3) Follow-up. After propensity score matching, all 90 patients were followed up after surgery for 16.5(range, 9.0-30.0)days. During the follow-up period, there was no significant difference in pain, seroma, incisional infection, readmission within 30 days after surgery getween two groups (P>0.05). No serious thrombotic events occurred in either group of patients, and no patient died. Conclusion:Compared with patients who discontinued antiplatelet agents alone before surgery, preoperative low molecular weight heparin bridging therapy after discontinua-tion of medication is safe and feasible for patients undergoing inguinal hernia repair who with long-term oral antiplatelet agents, in additon to less plasma drainage tubes using during the operation and without more risk of bleeding, but more postoperative pain.

BACKGROUND:Emodin has a variety of pharmacological activities such as anti-inflammatory,anti-viral and anti-oxidative stress,and also has a certain protective effect on sepsis-induced myocardial injury,but its mechanism of action is still unclear.OBJECTIVE:To investigate whether emodin can improve myocardial injury in septic mice by promoting autophagy.METHODS:Thirty-two male Kunming mice were divided into sham operation group(n=4),sham operation+emodin group(n=4),model group(n=8),model+emodin group(n=8),and emodin+3-methyladenine group(n=8).The myocardial injury model of septic mice was constructed by cecal ligation and puncture.3-methyladenine(10 mg/kg)was injected intraperitoneally 1 hour before modeling.Emodin(20 mg/kg)was injected intraperitoneally 30 minutes before modeling,and the other groups were injected with the same amount of normal saline at the same time point.Blood and myocardial samples were collected from all mice 24 hours after surgery.ELISA was used to detect the levels of brain natriuretic peptide and cardiac troponin Ⅰ in serum.Western blot assay was used to detect the protein expression of LC3B,Beclin-1,and p62 in myocardial tissue.Hematoxylin-eosin staining was used to observe the pathological changes in myocardial tissue.Ultrasound was used to evaluate the cardiac function of mice.RESULTS AND CONCLUSION:(1)Compared with the sham operation group,there was no significant difference in the levels of serum brain natriuretic peptide,cardiac troponin Ⅰ,and the protein expression of myocardial autophagy proteins LC3Ⅱ/LC3Ⅰ and p62 in the sham operation+emodin group(P＞0.05).(2)Compared with the sham operation+emodin group,the levels of serum brain natriuretic peptide and cardiac troponin I were significantly increased in the model group(P＜0.05).Compared with the model group,the levels of serum brain natriuretic peptide and cardiac troponin I were decreased in the model+emodin group(P＜0.05).(3)Compared with the model group,the expression of LC3Ⅱ/LC3Ⅰ and Beclin-1 protein was increased and the expression of p62 protein was decreased in the myocardial tissue of the model+emodin group(P＜0.05).Compared with the model+emodin group,the expression of LC3Ⅱ/LC3Ⅰ and Beclin-1 protein decreased and the expression of p62 protein increased in the emodin+3-methyladenine group(P＜0.05).(4)The myocardial fibers in the sham operation group were normal,the myocardial fibers in the model group were disordered with a large number of inflammatory cell infiltration,the myocardial fibers in the model+emodin group were slightly disordered,and some vacuolar changes were observed.The myocardial fibers were disordered,and more inflammatory cell infiltration was observed in the emodin+3-methyladenine group.(5)Compared with the sham operation group,the left ventricular short axis shortening rate and left ventricular ejection fraction were decreased in the model group(P＜0.05).Compared with the model group,the left ventricular short axis shortening rate and left ventricular ejection fraction were increased in the model+emodin group(P＜0.05).Compared with the model+emodin group,the left ventricular ejection fraction of emodin+3-methyladenine group was decreased(P＜0.05),and the left ventricular short axis shortening rate was reduced but not statistically significant(P＞0.05).(6)The above results indicate that emodin pretreatment can improve myocardial injury and myocardial dysfunction in septic mice by promoting autophagy.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Lens injury is an important etiological factor in the reduction of visual function following ocular trauma.Currently, there are no clear standards for the classification of lens injury, and comprehensive diagnostic tools are lacking.This deficiency leads to numerous controversies and challenges in critical areas, such as diagnosis and preoperative evalution, timing of surgery, surgical strategy, and assessment of postoperative prognosis.Tomographic imaging technology, such as computed tomography, magnetic resonance imaging, optical coherence tomography, has introduced a new dimension to the evaluation of lens injury, which is crucial for assessing the transparency, texture, location, morphology, and integrity of the lens, as well as the zonules and nearby intraocular structures.However, the use of tomographic imaging technology is somewhat limited due to the limitations of relying on a single method.With the ongoing advancement of imaging technologies and the rapid development of big data and artificial intelligence, tomographic imaging will become an increasingly essential tool in the future management of lens injury.Our expert group reviewed the epidemiological characteristics and classification of lens injury and the major challenges currently faced in the diagnosis and treatment of lens injury, and provided expert recommendations mainly focusing on the application, shortcomings and limitations of current tomographic imaging technology in the diagnosis and treatment of lens injury, and future development directions.

Objective:To investigate the clinical value of low molecular weight heparin bridging therapy for patients undergoing inguinal hernia repair who with long-term oral antiplatelet agents.Methods:The propensity score matching and retrospective cohort study was conducted. The clinical data of 126 patients undergoing tension-free inguinal hernia repair who with long-term oral antiplatelet agents and admitted to Sichuan Academy of Medical Sciences & Sichuan Provincial People′s Hospital (Affiliated Hospital of University of Electronic Science and Technology of China) from January 2017 to January 2025 were collected. There were 120 males and 6 females, aged (74±9)years. Of the 126 patients, 77 patients who discontinued antiplatelet agents alone before inguinal hernia repair were set as the drug withdrawal group, and 49 patients who discontinued antiplatelet agents with low molecular weight heparin bridging therapy before inguinal hernia repair were set as the bridging group. Observation indicators: (1) propensity score matching and comparison of general data of patients between the two groups after matching; (2) intraoperative and postopera-tive conditions; (3) follow-up. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test or Fisher exact probability. Propensity score matching was performed using the 1∶1 nearest neighbor matching method. The caliper value was set as 0.1. Results:(1) Propensity score matching and comparison of general data of patients between the two groups after matching. Of the 126 patients, 90 patients were success-fully matched, with 45 cases in each of the drug withdrawal group and the bridging group. After propensity score matching, the elimination of hernia ring size, activated partial thromboplasmin time and surgical method factors confounding bias ensured comparability. (2) Intraoperative and postoperative conditions. After propensity score matching, patients using plasma drainage tubes during the operation in the drug withdrawal group and the bridging group were 8 and 1, respec-tively, showing a significant difference between the two groups ( P<0.05). The visual analogue scale scores of patients in the drug withdrawal group and the bridging group at 48 hours after surgery were 2(range, 1-2) and 2(range, 2-3), respectively, showing a significant difference between the two groups ( Z=-2.57, P<0.05). (3) Follow-up. After propensity score matching, all 90 patients were followed up after surgery for 16.5(range, 9.0-30.0)days. During the follow-up period, there was no significant difference in pain, seroma, incisional infection, readmission within 30 days after surgery getween two groups (P>0.05). No serious thrombotic events occurred in either group of patients, and no patient died. Conclusion:Compared with patients who discontinued antiplatelet agents alone before surgery, preoperative low molecular weight heparin bridging therapy after discontinua-tion of medication is safe and feasible for patients undergoing inguinal hernia repair who with long-term oral antiplatelet agents, in additon to less plasma drainage tubes using during the operation and without more risk of bleeding, but more postoperative pain.

Lens injury is an important etiological factor in the reduction of visual function following ocular trauma.Currently, there are no clear standards for the classification of lens injury, and comprehensive diagnostic tools are lacking.This deficiency leads to numerous controversies and challenges in critical areas, such as diagnosis and preoperative evalution, timing of surgery, surgical strategy, and assessment of postoperative prognosis.Tomographic imaging technology, such as computed tomography, magnetic resonance imaging, optical coherence tomography, has introduced a new dimension to the evaluation of lens injury, which is crucial for assessing the transparency, texture, location, morphology, and integrity of the lens, as well as the zonules and nearby intraocular structures.However, the use of tomographic imaging technology is somewhat limited due to the limitations of relying on a single method.With the ongoing advancement of imaging technologies and the rapid development of big data and artificial intelligence, tomographic imaging will become an increasingly essential tool in the future management of lens injury.Our expert group reviewed the epidemiological characteristics and classification of lens injury and the major challenges currently faced in the diagnosis and treatment of lens injury, and provided expert recommendations mainly focusing on the application, shortcomings and limitations of current tomographic imaging technology in the diagnosis and treatment of lens injury, and future development directions.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To understand the etiological surveillance and drug resistance characteristics of Legionella pneumophila (LP) from the aqueous environment of public places in Shanghai, from 2011 to 2020, and provide evidence for surveillance of the disease. Methods:Environmental water samples were systematically collected from public venues in urban and suburban districts of Shanghai for LP surveillance. All the identified LP isolates underwent a series of testings including serotyping, pulsed field gel electrophoresis (PFGE), sequence-based typing, and antimicrobial susceptibility testing. χ2 test or Cochran-Armitage trend tests were used for statistical analysis and for temporal resistance patterns. Results:Among 6 263 water samples, the LP-positive rate was 20.93% (1 311/6 263). The positivity rate decreased from 24.98% (287/1 149) in 2011-2012 to 20.02% (1 024/5 114) in 2013-2020 ( χ2=13.92, P<0.001), with the highest monthly positivity observed from June to August (23.79%, 745/3 132). A total of 1 365 LP strains were isolated, of which 912 were further characterized, including 10 serotypes, 149 PFGE patterns, and 33 sequence types (ST). The predominant serotype was Lp1 (86.84%, 792/912), and the dominant ST was ST752 (29.50%, 269/912). ST clustering revealed two major clonal groups CG1 and CG2, accounting for 91.12% (831/912) of the isolates. The 190 LPs involved in the drug sensitivity test showed three resistance profiles: azithromycin resistance (31.05%, 59/190), ciprofloxacin resistance (0.53%, 1/190) and azithromycin+ciprofloxacin resistance (0.53%, 1/190). Azithromycin-resistant strains were predominantly ST1 (64.41%, 38/59). The antimicrobial resistance rate showed a significant decline, from 48.65% (18/37) in 2011-2012 to 28.10% (43/153) in 2013-2020 ( χ2=9.38, P=0.002). Conclusions:Compared to from 2011 to 2012, both the positivity rate and antimicrobial resistance prevalence of LP in public aqueous environments of Shanghai exhibited an overall decline from 2013 to 2020. The predominant types of LP were serotype Lp1 and sequence type ST752, with notable high-level resistance to azithromycin. Measures as enhancing the enforcement of water safety regulations and prioritizing surveillance of azithromycin resistance in LP were recommended to mitigate public health risks.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.